Schizophrenia (SCZ) genomics has achieved unprecedented advances. A decade ago, there was perhaps one solid finding, and there are now 130+ loci that meet consensus criteria for significance and replication. The Swedish SCZ Study (S3) and its investigators were centrally important to these advances. Genetic analyses of S3 samples have been major parts of multiple high profile papers. We cooperate well with other groups, and are leaders in the PGC. There is more to do. Thus, this is a competitive renewal for the S3 project. The S3 is arguably the largest and best-characterized SCZ sample anywhere: we propose to make it larger and more informative. In each prior R01, we accomplished far more than we proposed. We now propose aims designed to maximize the informativeness of S3 by doubling its size, adding cognitive phenotypes, and innovative analyses. Critically, this work is multi-funded and maximizes contributions from others and minimizes NIH budgetary requests. Specific Aims (1) Augment S3 dataset by doubling the sample size, add cognitive phenotypes, conduct comprehensive genomic characterization, impute to a Sweden-specific reference panel, and add new Swedish register linkages. Output: a large and comprehensive dataset ready for analysis. (2) Analysis: increase knowledge of the genetic basis of SCZ. Integrate data from Aim 1 with all other world samples to discover compellingly associated loci. Add multi-omic integration: combine, annotate, and rigorously evaluate results with all available epigenomic and gene expression data (e.g., CommonMind, psychENCODE). Output: SCZ associations across the allelic spectrum, specific hypotheses about the immediate biological impact of genetic variation implicated in SCZ. Successful completion of this work - capitalizing on cutting-edge technologies and a highly productive decade- long collaboration - is highly likely to advance knowledge of SCZ by identifying more loci, providing specific biological hypotheses, and understanding of GxE action and interaction. This study is preclinical. Although not proposed here due to complexity and expense, we will immediate prioritize any potential therapeutic target via collaborations (e.g., with Dr Sullivan's UNC colleague and antipsychotic expert Dr Bryan Roth). The work proposed is highly efficient / cost-effective due to our multi-funding model. We have minimized costs (while maximizing the science we can achieve) via multiple strategic partnerships. We use consultancies to bring well-funded investigators into S3. We routinely use multiple technologies to enhance collaboration.